Testing the Predictive Validity of the Hendrich II Fall Risk Model

Area of Science:

• Healthcare Informatics
• Patient Safety
• Clinical Assessment

Background:

• Electronic medical records (EMR) systems compile patient fall risk data.
• Validating fall-risk assessment tools is crucial for patient safety.
• Data between admission and falls can be used for tool validation.

Purpose of the Study:

• To test the predictive validity of the Hendrich II Fall Risk Model.
• To determine the optimal time point for fall risk assessment during hospitalization.
• To identify significant factors contributing to patient falls.

Main Methods:

• Extracted Hendrich II Fall Risk Model scores at three time points: admission, maximum score, and pre-fall/discharge.
• Examined predictive validity using seven indicators.
• Employed logistic regression analysis to identify significant fall risk factors.

Main Results:

• The maximum fall-risk score between admission and fall/discharge demonstrated the highest predictive performance.
• Confusion or disorientation was a significant predictor of falls.
• Poor ability to rise from a sitting position was also a significant risk factor.

Conclusions:

• Assessing the maximum fall risk score during hospitalization improves fall prediction.
• Cognitive status (confusion/disorientation) and physical mobility are key modifiable fall risk factors.
• EMR data can effectively validate fall risk assessment tools.